Big data & advertising: Prediction over supposition

Big data and its influence is currently cementing its place in the marketing landscape as the one of the most valuable resources for the upcoming century. That said, it is time to take a look at how big data can influence advertising campaigns and efficiency and how it could continue to evolve in the future.

Big data is a term created by Bitkom in 2012 when they identified it as an upcoming digital trend. It describes the shift from collecting enormous amount of information in the past centuries, to using this information or data, to improve almost any level of society. From company workflows to city traffic and even the prediction of social behavior, big data is seen as a key game changer.

However, in online advertising the concept behind big data is not new at all. Obvious examples are those companies which have collected the most data over the past few years; Google, Amazon, Facebook and so on. Targeted advertising is key to their success in advertising as they have been able to offer advertisers more and more accurate user profiles to reduce wastage. Nevertheless, the main difference between these advertising strategies is that big data is used to predict future behavior. In short; Amazon customers won’t be defined by what they bought, the will be defined by what they probably will buy.

This process still contains algorithms, patterns and browser cookies, and with the growth of mobile data it is becoming significantly harder for the public to stay offline. Therefore even more data will be gathered in the next couple of years, which will help companies to pinpoint their exact target group. Right now, tools like ‘Google Now’ records a user’s movements from their home to their workplace and tailor its recommendation and the information it offers to that user accordingly. This type of data would also make easy to catch the user with a relevant advertisement, such a mobile ad or even a digital billboard. After a certain time period ‘Google Now’ can also notice other social behavior patterns. For example, when the user goes to the cinema, or what restaurant or bar he regularly visits. This data could then become combined with his digital footprint, linking for instance his mobile shopping choices, search and social networking behavior.

Ultimately, computer prediction will become more accurate about people, making big data the number one resource for companies who want to advertise, as well as advertisers. However, the biggest advantage – predictability – is also the biggest disadvantage of big data if it is used exclusively. By targeting users who buy advertised products, success could become more attached to driving sales than to creating a convincing and creative campaign. Companies who also want to reinvent or redefine who they attract could also find their sources of data lacking, giving companies such as Google a lot of potential revenue by selling the big data they have. Advertising to particular target groups, which could be less likely to be convinced, could also decline as could the general awareness of a brand if its targeting does not widen its audience. Already we see that an internet user’s behavior is limited somewhat through search engines, where the ability to stumble upon something new and exciting is at odds with a search engine’s roll to give a user exactly what they ask for.

This lack of surprise and the subsequent idea that big data could limit our surprises further shows the need for balance in the market. People are predictable, but this does not mean they necessarily like it or do not enjoy being surprised and entertained by the unexpected. As advertisers this is something we should not forget – whilst we need to target the right audience, we should always try to surprise and rouse a brand’s potential audience too.